code: def scatter_nd_update(
      ref, indices, updates, use_locking=True, name=None
  ):
      r"""Applies sparse `updates` to individual values or slices in a Variable.
  
      `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
  
      `indices` must be integer tensor, containing indices into `ref`.
      It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
  
      The innermost dimension of `indices` (with length `K`) corresponds to
      indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
      dimension of `ref`.
  
      `updates` is `Tensor` of rank `Q-1+P-K` with shape:
  
      ```
      [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
      ```
  
      For example, say we want to update 4 scattered elements to a rank-1 tensor to
      8 elements. In Python, that update would look like this:
  
      ```python
          ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
          indices = tf.constant([[4], [3], [1] ,[7]])
          updates = tf.constant([9, 10, 11, 12])
          update = tf.compat.v1.scatter_nd_update(ref, indices, updates)
          with tf.compat.v1.Session() as sess:
            print sess.run(update)
      ```
  
      The resulting update to ref would look like this:
  
          [1, 11, 3, 10, 9, 6, 7, 12]
  
      See `tf.scatter_nd` for more details about how to make updates to
      slices.
  
      Args:
        ref: A Variable.
        indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
          A tensor of indices into ref.
        updates: A `Tensor`. Must have the same type as `ref`.
          A Tensor. Must have the same type as ref. A tensor of updated
          values to add to ref.
        use_locking: An optional `bool`. Defaults to `True`.
          An optional bool. Defaults to True. If True, the assignment will
          be protected by a lock; otherwise the behavior is undefined,
          but may exhibit less contention.
        name: A name for the operation (optional).
  
      Returns:
        The value of the variable after the update.
      """
      if ref.dtype._is_ref_dtype:
          return gen_state_ops.scatter_nd_update(
              ref, indices, updates, use_locking, name
          )
      return ref._lazy_read(
          gen_state_ops.resource_scatter_nd_update(  # pylint: disable=protected-access
              ref.handle,
              indices,
              ops.convert_to_tensor(updates, ref.dtype),
              name=name,
          )
      )
  
imports: from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import gen_resource_variable_ops
  from tensorflow.python.ops import gen_state_ops
  from tensorflow.python.ops import state_grad
  from tensorflow.python.ops.gen_state_ops import *
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.deprecation import deprecated
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 10 for calling a global function with example usage: scatter_nd_update(ref, indices, updates, use_locking, name) and returns: ref, gen_state_ops